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  • PLS_Toolbox FAQ
    Wiki Browse EigenGuide Videos Search for Keyword s Issue How are the error bars calculated for a regression model and can they be related to a confidence limit confidence in the prediction Possible Solutions The error bars reported for inverse least squares models and from the ils esterror function represent the estimation error for each prediction see Faber N M and Bro R Chemomem and Intell Syst 61 133 149

    Original URL path: http://eigenvector.com/faq/index.php?id=134 (2016-04-27)
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  • PLS_Toolbox FAQ
    more of the following Use the latest version of PLS Toolbox In some cases we ve been able fix problems associated with Mac performance Try changing the Java Heap Size from the Matlab Preferences Java Heap Memory menu item Select the largest amount available e g 256MB For Matlab versions 2011a and newer disable Screen Menus via the java opts file Save all of your work then from the Matlab Command window enter edit fullfile matlabroot bin maci64 java opts then add the following line Dapple laf useScreenMenuBar false save the file and restart Matlab Try using a different version of Matlab Depending on the version of hardware and PLS Toolbox some older versions of Matlab may work better than others Newer versions 2011a 2012a seem to require disabling of screen menus and increased Heap size steps 2 and 3 In general we recommend the newest version of Matlab available Hide the model cache Sometimes the java tree component seems to slow things down Hiding the cache when not in use may help From the Analysis Tools menu select View Cache Hide Cache Viewer Set javaopts to use Quartz rendering via the java opts as above Dapple awt graphics UseQuartz

    Original URL path: http://eigenvector.com/faq/index.php?id=158 (2016-04-27)
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  • PLS_Toolbox FAQ
    for FAQ Frequently Asked Questions Browse FAQ Browse Documentation Wiki Browse EigenGuide Videos Search for Keyword s Issue How do I assign classes for samples in a DataSet Possible Solutions In many applications the groups or classes of samples in a data set are critical to the modeling and or interpretation of results In PCA scores plots can be quickly interpreted for clustering if samples can be labeled colored according

    Original URL path: http://eigenvector.com/faq/index.php?id=44 (2016-04-27)
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  • PLS_Toolbox FAQ
    model from a class set other than the first Possible Solutions As of version 6 5 of PLS Toolbox and Solo the class set to model can be selected from either the Class Groups interface in PLSDA or in the method settings See the Edit Options Method Options menu or the Method Options toolbar button In earlier versions of the software the following instructions indicate how to use other class sets Although DataSet objects allow you to use multiple class sets to identify different groupings of your samples most classification methods e g SIMCA PLSDA SVMDA KNN only allow you to use the first class set as the basis for the model To use a class set other than the first as the basis for a classification model you need to copy the class set you want into the first set 1 Start up the DataSet editor double click the X block icon go the Row Labels tab On that tab you will find the class sets listed in a pull down menu just below the Class column header 2 Select the class set you want to build your model from in the pull down menu 3 Right click the

    Original URL path: http://eigenvector.com/faq/index.php?id=137 (2016-04-27)
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  • PLS_Toolbox FAQ
    Keyword s Issue How do I choose between the different cross validation leave out options Possible Solutions There are many attributes that influence selection of the appropriate cross validation scheme for a given situation The ordering of the samples in the dataset The total number of objects and variables in the dataset The presence or lack thereof of replicate samples in the dataset The specific objective s of the analysis

    Original URL path: http://eigenvector.com/faq/index.php?id=108 (2016-04-27)
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  • PLS_Toolbox FAQ
    to sufficiently evaluate PLS Toolbox or Solo during your demo period please contact us at orders eigenvector com Please provide details as to why you are requesting a demo license extension e g extenuating circumstances Regrettably requests without such explanations can not be granted Student Demos Students enrolled at degree granting institutions are eligible for longer demo periods These licenses are intended to allow students to use the software while

    Original URL path: http://eigenvector.com/faq/index.php?id=29 (2016-04-27)
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  • PLS_Toolbox FAQ
    classify into one group or the other and is the same axis as the predicted y value As you increase the threshold the specificity increases i e the false positive rate DECREASES you are less likely to mistakenly call something in class when it is not Likewise the sensitivity decreases i e the false negative rate INCREASES you are more likely to call something not in class when it is actually in class If you put the threshold all the way at the right very high threshold value everything will be classified as not in class The sensitivity is zero none of the true positives were detected 100 false negative rate but the specificity is 100 you did not have any false positives none of the not in class samples were marked in class If you put the threshold all the way to the left very low threshold value everything will be classified as in class The sensitivity is 100 all of the true positives were detected 0 false negative rate but the specificity is zero all of your not in class samples were false positives The point where the two curves meet is the balance between false positives and false negatives The higher up the y axis this point the better the model The exact value where they meet is 1 misclassification rate Useful hint in the overall plot view you can right click on a given sub plot to view a larger version of it The ROC curves show similar information in a different format The motion from left to right across the sensitivity specificity curves is the same as moving CLOCKWISE around the ROC curve the ROC curve is just the specificity plotted against the sensitivity with no errors being the point in the upper left corner

    Original URL path: http://eigenvector.com/faq/index.php?id=106 (2016-04-27)
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  • PLS_Toolbox FAQ
    the following error when I loaded a dataset into Matlab Warning Loaded dataset object newer than present constructor Dataset object converted to structure This happened because the DataSet being loaded is of a new version than the current DataSet Object There are a couple of ways to get around this 1 Try using the struct2dataset function to generate a DataSet from the structure newdso struct2dataset oldstruct 2 If you only

    Original URL path: http://eigenvector.com/faq/index.php?id=88 (2016-04-27)
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